OCR+ECAR-Pipeline

Wells removed. Out od 140-160 mmHg interval

Plate	N_out	Wells
PBMC-DB2#9A_2+HF#31A 13-08-2018	7	B07 C06 D06 D07 E06 F09 G06
PBMC-HF#10A 30-10-2017	5	B08 C08 C09 E11 H01
PBMC-HF#10B 18-12-2017	30	B03 B07 B08 B09 B10 C04 C07 C08 C09 C10 C11 D01 D02 D04 D06 D07 D08 D09 E01 E07 E08 E09 E10 E11 F07 F08 F09 F10 G08 G10
PBMC-HF#11A+HC#Y 28-11-2017	25	A01 A08 B10 C07 C08 C09 D08 D09 D10 D11 D12 E04 E07 E08 E09 E10 E11 F08 F10 F12 G05 G07 H07 H08 H11
PBMC-HF#11B+HF#15A 15-01-2018	18	B08 C05 C08 C09 C10 C11 D03 D06 D08 D09 D10 E04 E07 E08 E09 E11 F08 G08
PBMC-HF#12A 06-12-2017	18	A09 B03 B08 C06 C07 C08 C09 D02 D03 D04 D06 D09 D11 E08 E11 F08 F09 G08
PBMC-HF#12B+HF#14A 22-01-2018	0
PBMC-HF#13A 26-02-2018	11	B01 B08 C07 C08 C09 D06 D07 E04 E10 F07 G03
PBMC-HF#13B 13-04-2018	37	A02 A03 A07 B01 B03 B05 B08 B09 B10 C06 C08 C10 D02 D05 D06 D07 D08 D12 E02 E03 E04 E05 E08 E09 E10 F06 F07 F08 F09 F10 F11 G03 G05 G11 H01 H11 H12
PBMC-HF#14B+HF#17A 12-03-2018	8	B02 C04 C07 D05 D06 D08 E05 H05
PBMC-HF#15B 19-03-2018	17	B04 B10 C04 C05 C07 C09 D03 D04 D05 D09 E02 E03 E05 E11 F03 F08 F09
PBMC-HF#17B+HF#27A 30-04-2018	3	D06 D07 H02
PBMC-HF#18A+DB2#2B_2 12-02-2018	26	A01 A03 A05 B03 C02 C04 D01 D02 D05 D06 D07 E04 E05 E06 E09 F01 F03 F08 G03 G04 G06 H01 H02 H03 H04 H06
PBMC-HF#18B 03-04-2018	4	B08 C03 D08 F08
PBMC-HF#19A 19-02-2018	5	A09 C07 D07 E09 F08
PBMC-HF#19B+HF#22A 09-04-2018	1	F08
CON-BIS-HF-1 14-08-2017	49	A07 A08 A09 A10 A11 A12 B07 B08 B09 B10 B11 B12 C05 C07 C08 C09 C10 C11 C12 D07 D08 D09 D10 D11 D12 E07 E08 E09 E10 E11 E12 F07 F08 F09 F10 F11 F12 G07 G08 G09 G10 G11 G12 H07 H08 H09 H10 H11 H12
PBMC-HF#1B 02-10-2017	17	A03 A09 B08 C06 C12 E03 E10 E12 F02 F09 F10 F11 F12 G03 H04 H08 H09
PBMC-HF#22B+HF#22Bb 28-05-2018	0
PBMC-HF#24A 12-04-2018	16	B10 C07 C08 C09 D02 D05 D06 D08 E04 E06 E07 E09 E10 F04 F08 F09
PBMC-HF#24B 04-06-2018	17	C07 C09 C10 D06 D07 D08 D10 E07 E09 E10 E11 F08 F09 F10 G08 G10 G11
PBMC-HF#25A 05-03-2018	9	B10 C06 C07 D04 D06 E04 E07 E08 E09
PBMC-HF#25B+HF#26A 23-04-2018	0
PBMC-HF#26B 11-06-2018	9	C08 D06 D07 D08 E08 E09 F07 F08 F09
PBMC-HF#27B+HF#27Bb 18-06-2018	3	A03 A07 D06
PBMC-HF#29A 08-05-2018	10	B08 C07 C09 D02 D06 E09 E10 E11 F07 F08
PBMC-HF#29B 25-06-2018	19	A07 A09 A10 A11 A12 B12 C12 D12 E12 F12 G11 G12 H03 H07 H08 H09 H10 H11 H12
PBMC-HF#31B+HF#42A 01-10-2018	32	A07 A08 A09 A10 B07 B09 B11 C08 C10 D08 E07 E08 E09 F07 F08 F09 F10 F11 F12 G01 G02 G03 G07 G08 G09 G10 G11 H07 H08 H09 H10 H11
PBMC-HF#32A+HF#32Ab 15-05-2018	2	A01 E08
PBMC-HF#32B 29-06-2018	2	E10 F09
PBMC-HF#33A 20-08-2018	1	C10
PBMC-HF#33B 08-10-2018	17	A03 A04 A09 A10 B01 B03 B04 B11 B12 C12 D12 E03 E04 E12 F11 G02 H10
PBMC-HF#34A+HC#19 25-09-2018	87	A02 A03 A04 A05 A06 A07 A08 A09 A10 A11 B01 B02 B03 B04 B05 B06 B07 B08 B10 B11 B12 C01 C02 C03 C04 C05 C06 C07 C08 C09 C10 C11 C12 D01 D02 D03 D04 D06 D07 D08 D09 D10 D11 D12 E01 E02 E03 E04 E05 E06 E07 E08 E09 E10 E11 E12 F01 F02 F03 F04 F05 F06 F08 F09 F10 F11 F12 G01 G02 G03 G04 G05 G06 G07 G08 G09 G10 G11 G12 H02 H03 H04 H05 H06 H07 H08 H11
PBMC-HF#34B 14-11-2018	7	A11 C08 D08 E08 E09 E11 F08
PBMC-HF#36A 27-08-2018	2	E07 H06
PBMC-HF#36B+HF#43A 15-10-2018	13	B04 C02 C06 C11 D01 D07 D08 E02 E05 F05 F08 H04 H06
PBMC-HF#39A 15-10-2018	0
PBMC-HF#39B 23-11-2018	24	A07 A08 B03 B08 C02 C06 C09 C11 D04 D10 D12 E02 E03 E04 F01 F02 F03 F08 F10 F11 G01 G06 G07 H11
PBMC-HF#3A+HF#3Ab 28-08-2017	0
PBMC-HF#3B+HC#2 17-10-2017	79	A01 A03 A05 A06 A07 A08 A09 A10 A11 B03 B04 B05 B06 B07 B08 B09 B10 B11 B12 C01 C02 C03 C04 C05 C06 C07 C08 C09 C10 C11 C12 D03 D04 D05 D06 D07 D08 D09 D10 D12 E01 E02 E03 E04 E05 E06 E07 E08 E09 E10 E12 F01 F02 F03 F04 F05 F06 F08 F09 F10 F11 F12 G01 G02 G03 G04 G05 G06 G07 G08 G09 G10 G11 H04 H05 H06 H08 H09 H10
PBMC-HF#40B+HF#55B 25-02-2019	18	A12 B01 B07 C02 D01 D06 D07 D08 E01 E03 E10 F01 F02 F08 F09 G01 G04 G06
PBMC-HF#42B+HF#46A 19-11-2018	3	F09 H05 H12
PBMC-HF#43B+HF#49A 05-12-2018	0
PBMC-HF#44A 29-10-2018	2	B10 C12
PBMC-HF#46B+HF#40A 07-01-2019	40	A04 A05 A06 A08 A10 A12 B03 B04 B06 B07 C01 C02 C04 C06 C08 C10 D03 D07 D08 D10 E01 E03 E05 E06 E07 E12 F01 F03 F06 F12 G02 G03 G04 G05 G08 G09 G10 H03 H05 H07
PBMC-HF#48A 05-11-2018	17	A01 A07 B09 C05 C08 D02 D07 D08 E04 E07 E10 F04 F08 F10 G01 G04 G08
PBMC-HF#48B 21-12-2018	8	C07 D07 D08 E03 E07 E08 F07 F08
PBMC-HF#49b 22-01-2019	18	A06 A07 B07 C07 C09 D06 D07 D08 D10 E06 E07 E08 E10 F08 F09 F10 G08 G11
PBMC-HF#4A+HF#4Ab 25-09-2017	6	A12 B09 C06 D07 E09 H01
PBMC-HF#4B 13-11-2017	1	E10
PBMC-HF#50A 26-11-2018	48	A03 A05 A06 A07 A08 A09 A11 A12 B01 B03 B04 B08 B10 C05 C06 C07 C09 C10 D02 D03 D04 D06 D08 D09 D10 E01 E02 E05 E07 E08 E10 F02 F05 F07 F08 F09 F10 G03 G04 G05 G06 G08 H01 H04 H05 H06 H10 H11
PBMC-HF#50B+DB2#11B_2 14-01-2019	13	B01 C05 D06 D07 D09 D10 E01 E03 E05 F02 F07 G09 H12
PBMC-HF#53A 02-01-2019	12	A07 A10 C08 C09 D07 D08 D09 E08 F01 F08 G05 H09
PBMC-HF#53B 20-02-2019	37	A08 B07 B08 B09 B10 B11 B12 C02 C03 C05 C07 C08 C09 C11 D01 D02 D06 D08 D09 D11 D12 E05 E08 E09 E10 E12 F06 F07 F08 F09 F10 F11 G05 G08 G10 H07 H11
PBMC-HF#55A 21-01-2019	35	A02 A03 A06 A07 A09 A10 B02 B03 B06 B07 B08 B09 C03 C08 C10 D05 D06 D08 D11 E02 E03 E05 E06 E07 E08 E09 F03 F11 G01 G05 G07 G08 G10 H04 H06
PBMC-HF#5A+HC#11 18-09-2017	1	F08
PBMC-HF#5B 06-11-2017	1	C09
PBMC-HF#7A 23-11-2017	2	A05 H05
PBMC-HF#8A+HC#X 23-10-2017	20	A02 B02 B05 C03 D02 D12 E02 E03 F01 F02 F03 F05 G02 G03 G04 G10 G11 G12 H04 H11
PBMC-HF#8B 11-12-2017	19	B08 C02 C06 C07 C08 C09 C10 C11 D06 D07 D08 D09 D11 E04 E09 E11 F07 F08 F10

Background measurements not included in OCR correction

Plate	N_out	Wells	Measurement
PBMC-DB2#9A_2+HF#31A 13-08-2018	18	D06 D07	1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9
PBMC-HF#10A 30-10-2017	9	H01	1 2 3 4 5 6 7 8 9
PBMC-HF#10B 18-12-2017	10	A01 D07 A12 D06	1 2 3 4 4 5 6 6 8 8
PBMC-HF#11A+HC#Y 28-11-2017	6	A01	4 5 6 7 8 9
PBMC-HF#11B+HF#15A 15-01-2018	3	D07	4 5 6
PBMC-HF#12A 06-12-2017	3	H01 D07	4 7 8
PBMC-HF#12B+HF#14A 22-01-2018	5	D07 D06	2 3 4 7 8
PBMC-HF#13A 26-02-2018	3	A01 D07 A12	1 4 6
PBMC-HF#13B 13-04-2018	9	H12	1 2 3 4 5 6 7 8 9
PBMC-HF#14B+HF#17A 12-03-2018	4	D06 D07 A01	1 3 4 7
PBMC-HF#15B 19-03-2018	0
PBMC-HF#17B+HF#27A 30-04-2018	3	H12 H01	1 2 2
PBMC-HF#18A+DB2#2B_2 12-02-2018	18	A01 H01	1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9
PBMC-HF#18B 03-04-2018	0
PBMC-HF#19A 19-02-2018	15	D07 H01	2 2 3 3 4 5 5 6 6 7 7 8 8 9 9
PBMC-HF#19B+HF#22A 09-04-2018	1	H01	1
CON-BIS-HF-1 14-08-2017	0
PBMC-HF#1B 02-10-2017	2	H01 H12	1 1
PBMC-HF#22B+HF#22Bb 28-05-2018	2	H01	4 6
PBMC-HF#24A 12-04-2018	1	H01	2
PBMC-HF#24B 04-06-2018	2	H01	1 3
PBMC-HF#25A 05-03-2018	0
PBMC-HF#25B+HF#26A 23-04-2018	2	H01 A01	1 3
PBMC-HF#26B 11-06-2018	3	A01 A12	1 1 2
PBMC-HF#27B+HF#27Bb 18-06-2018	9	H12 D07	1 2 2 3 5 5 8 8 9
PBMC-HF#29A 08-05-2018	4	H01 H12 D07	2 3 4 9
PBMC-HF#29B 25-06-2018	5	H12 A01 H01	6 7 7 7 8
PBMC-HF#31B+HF#42A 01-10-2018	3	A12 D06	2 2 4
PBMC-HF#32A+HF#32Ab 15-05-2018	4	A01 D07	1 2 3 7
PBMC-HF#32B 29-06-2018	1	A01	1
PBMC-HF#33A 20-08-2018	0
PBMC-HF#33B 08-10-2018	10	D07 A01	1 2 3 4 5 6 7 8 9 9
PBMC-HF#34A+HC#19 25-09-2018	0
PBMC-HF#34B 14-11-2018	1	D07	6
PBMC-HF#36A 27-08-2018	3	H12	1 8 9
PBMC-HF#36B+HF#43A 15-10-2018	6	D07	2 3 5 6 7 8
PBMC-HF#39A 15-10-2018	6	H01 H12	1 3 7 8 8 9
PBMC-HF#39B 23-11-2018	6	H01 D06	4 6 7 8 8 9
PBMC-HF#3A+HF#3Ab 28-08-2017	2	A01 A12	1 5
PBMC-HF#3B+HC#2 17-10-2017	0
PBMC-HF#40B+HF#55B 25-02-2019	0
PBMC-HF#42B+HF#46A 19-11-2018	0
PBMC-HF#43B+HF#49A 05-12-2018	3	H01	1 2 5
PBMC-HF#44A 29-10-2018	2	H12	1 3
PBMC-HF#46B+HF#40A 07-01-2019	1	D07	2
PBMC-HF#48A 05-11-2018	9	A01	1 2 3 4 5 6 7 8 9
PBMC-HF#48B 21-12-2018	0
PBMC-HF#49b 22-01-2019	6	A01	4 5 6 7 8 9
PBMC-HF#4A+HF#4Ab 25-09-2017	5	A12 D07	1 2 8 8 9
PBMC-HF#4B 13-11-2017	3	H01 D06 D07	1 9 9
PBMC-HF#50A 26-11-2018	0
PBMC-HF#50B+DB2#11B_2 14-01-2019	9	H12	1 2 3 4 5 6 7 8 9
PBMC-HF#53A 02-01-2019	0
PBMC-HF#53B 20-02-2019	18	D06 D07	1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9
PBMC-HF#55A 21-01-2019	9	D06	1 2 3 4 5 6 7 8 9
PBMC-HF#5A+HC#11 18-09-2017	9	D07	1 2 3 4 5 6 7 8 9
PBMC-HF#5B 06-11-2017	1	H12	9
PBMC-HF#7A 23-11-2017	5	D07	2 3 4 5 6
PBMC-HF#8A+HC#X 23-10-2017	5	D07 H01	6 7 8 8 9
PBMC-HF#8B 11-12-2017	1	D07	2

Background measurements not included in ECAR correction

Plate	N_out	Wells	Measurement
PBMC-DB2#9A_2+HF#31A 13-08-2018	4	H12 D06	1 2 3 6
PBMC-HF#10A 30-10-2017	1	D06	7
PBMC-HF#10B 18-12-2017	4	D06 D07	2 4 6 7
PBMC-HF#11A+HC#Y 28-11-2017	2	A12 D07	5 7
PBMC-HF#11B+HF#15A 15-01-2018	2	D06 D07	7 7
PBMC-HF#12A 06-12-2017	2	D07	8 9
PBMC-HF#12B+HF#14A 22-01-2018	2	A01	1 5
PBMC-HF#13A 26-02-2018	1	H12	9
PBMC-HF#13B 13-04-2018	0
PBMC-HF#14B+HF#17A 12-03-2018	2	A01 A12	2 2
PBMC-HF#15B 19-03-2018	3	D06	4 6 10
PBMC-HF#17B+HF#27A 30-04-2018	2	D06 D07	7 7
PBMC-HF#18A+DB2#2B_2 12-02-2018	0
PBMC-HF#18B 03-04-2018	0
PBMC-HF#19A 19-02-2018	0
PBMC-HF#19B+HF#22A 09-04-2018	2	D06 A01	1 6
CON-BIS-HF-1 14-08-2017	5	A12 H12	1 2 2 3 3
PBMC-HF#1B 02-10-2017	0
PBMC-HF#22B+HF#22Bb 28-05-2018	2	H01 D07	4 7
PBMC-HF#24A 12-04-2018	2	D07 D06	2 7
PBMC-HF#24B 04-06-2018	0
PBMC-HF#25A 05-03-2018	2	D06	7 8
PBMC-HF#25B+HF#26A 23-04-2018	2	H12 D07	5 7
PBMC-HF#26B 11-06-2018	0
PBMC-HF#27B+HF#27Bb 18-06-2018	4	A12 D06 D07	4 5 7 7
PBMC-HF#29A 08-05-2018	3	D07 D06	4 7 7
PBMC-HF#29B 25-06-2018	12	H01 H12 A01	2 2 5 5 6 6 7 7 8 8 9 9
PBMC-HF#31B+HF#42A 01-10-2018	1	D06	7
PBMC-HF#32A+HF#32Ab 15-05-2018	3	A01 H01	3 4 5
PBMC-HF#32B 29-06-2018	1	D07	7
PBMC-HF#33A 20-08-2018	7	D07 A12 D06	1 2 3 4 6 7 7
PBMC-HF#33B 08-10-2018	4	A12 A01	1 6 8 8
PBMC-HF#34A+HC#19 25-09-2018	5	A01 H12 D06	2 2 3 7 9
PBMC-HF#34B 14-11-2018	2	D07 D06	4 7
PBMC-HF#36A 27-08-2018	1	A12	1
PBMC-HF#36B+HF#43A 15-10-2018	0
PBMC-HF#39A 15-10-2018	0
PBMC-HF#39B 23-11-2018	1	A12	2
PBMC-HF#3A+HF#3Ab 28-08-2017	1	A12	5
PBMC-HF#3B+HC#2 17-10-2017	9	H12	1 2 3 4 5 6 7 8 9
PBMC-HF#40B+HF#55B 25-02-2019	0
PBMC-HF#42B+HF#46A 19-11-2018	12	H12 A12	1 2 3 3 4 4 5 5 6 7 8 9
PBMC-HF#43B+HF#49A 05-12-2018	0
PBMC-HF#44A 29-10-2018	1	D07	7
PBMC-HF#46B+HF#40A 07-01-2019	3	D07 D06	3 7 7
PBMC-HF#48A 05-11-2018	2	A01 D06	1 3
PBMC-HF#48B 21-12-2018	0
PBMC-HF#49b 22-01-2019	4	A01	2 3 5 6
PBMC-HF#4A+HF#4Ab 25-09-2017	6	A12 D06 D07	1 2 7 7 8 9
PBMC-HF#4B 13-11-2017	10	A01 H12	3 3 4 4 5 5 6 6 8 9
PBMC-HF#50A 26-11-2018	0
PBMC-HF#50B+DB2#11B_2 14-01-2019	3	H01	2 3 9
PBMC-HF#53A 02-01-2019	3	A01	4 6 9
PBMC-HF#53B 20-02-2019	0
PBMC-HF#55A 21-01-2019	3	A12 A01 H12	2 6 6
PBMC-HF#5A+HC#11 18-09-2017	0
PBMC-HF#5B 06-11-2017	1	A12	9
PBMC-HF#7A 23-11-2017	1	H12	8
PBMC-HF#8A+HC#X 23-10-2017	4	H01 A12	1 3 4 9
PBMC-HF#8B 11-12-2017	0

———– OCR ———–

Filter out no relevant samples

## `summarise()` has grouped output by 'sample_id'. You can override using the `.groups` argument.

## [1] "Samples removed due lack of usable measurements(less than 8  in any interval)"

## [1] "19-HC | 25-09-2018"  "2-HC | 17-10-2017"   "34A-HF | 25-09-2018" "3B-HF | 17-10-2017"  "42A-HF | 01-10-2018"

## [1] "Samples removed because there are no 4 intervals )"

##  [1] "15B-HF | 19-03-2018"                          "19-HC | 25-09-2018"                           "2-HC | 17-10-2017"                           
##  [4] "31Bt-HF | 01-10-2018"                         "32Ab- | 15-05-2018"                           "34A-HF | 25-09-2018"                         
##  [7] "4 post-Conbis  | 13-11-2017"                  "48 post-ConBis  | 21-12-2018"                 "7A-CHF | 23-11-2017"                         
## [10] "FCCP-CONBIS-29POST-29POST | 25-06-2018"       "Glycolysis-CONBIS-29POST-29POST | 25-06-2018" "Glycolysis-DB-26-26 | 06-12-2017"            
## [13] "Glycolysis-DB-26-26 | 19-02-2018"             "Oligomycin-CONBIS-29POST-29POST | 25-06-2018"

Identyfy Outliars

Removed outliars in each iteration:

## 1  Point outliares:  2031 -- 8.289796 % 
## 2  Point outliares:  750 -- 3.061224 % 
## 3  Point outliares:  291 -- 1.187755 % 
## 4  Point outliares:  140 -- 0.5714286 % 
## 5  Point outliares:  70 -- 0.2857143 % 
## 6  Point outliares:  42 -- 0.1714286 % 
## 7  Point outliares:  16 -- 0.06530612 % 
## 8  Point outliares:  6 -- 0.0244898 % 
## 9  Point outliares:  5 -- 0.02040816 % 
## 10  Point outliares:  4 -- 0.01632653 % 
## 11  Point outliares:  1 -- 0.004081633 % 
## 12  Point outliares:  1 -- 0.004081633 % 
## 13  Point outliares:  1 -- 0.004081633 % 
## 14  Point outliares:  0 -- 0 % 
## Total single point outliars:  13.70612 %

## `summarise()` has grouped output by 'sample_id'. You can override using the `.groups` argument.

Amount of removed outliers across samples

sample_id	percentage
Y-HC | 28-11-2017	39.18 %
32Ab-HF | 15-05-2018	31.85 %
39 post-ConBis | 23-11-2018	29.80 %
29A-HF | 08-05-2018	26.21 %
26A-HF | 23-04-2018	25.00 %
33A-HF | 20-08-2018	24.29 %
18A-HF | 12-02-2018	24.24 %
22Bb-HF | 28-05-2018	23.33 %
15A-HF | 15-01-2018	23.27 %
53 post-ConBis | 20-02-2019	22.33 %
27A-HF | 30-04-2018	21.94 %
49A-HF | 05-12-2018	21.11 %
33B-HF | 08-10-2018	20.98 %
19B-HF | 09-04-2018	20.83 %
26B-HF | 11-06-2018	20.56 %
10A-HF | 30-10-2017	19.17 %
18B-HF | 03-04-2018	18.99 %
4B-HF | 13-11-2017	18.89 %
9A_2-DB2 | 13-08-2018	18.42 %
11-HC | 18-09-2017	18.41 %
27Bb-HF | 18-06-2018	18.08 %
3Ab-HF | 28-08-2017	18.06 %
55B-HF | 25-02-2019	17.59 %
32A-HF | 15-05-2018	17.22 %
55A-HF | 21-01-2019	17.11 %
49B-HF | 22-01-2019	16.95 %
25B-HF | 23-04-2018	16.94 %
5B-HF | 06-11-2017	16.71 %
17B-HF | 30-04-2018	16.52 %
40B-HF | 25-02-2019	16.12 %
22A-HF | 09-04-2018	16.10 %
46B-HF | 07-01-2019	15.98 %
11B_2-DB2 | 14-01-2019	15.90 %
11A-HF | 28-11-2017	15.70 %
12B-HF | 22-01-2018	15.60 %
24A-HF | 12-04-2018	15.29 %
14B-HF | 12-03-2018	14.60 %
7A-HF | 23-11-2017	14.07 %
48 pre- | 05-11-2018	13.92 %
2B_2-DB2 | 12-02-2018	13.62 %
13A-HF | 26-02-2018	13.51 %
14A-HF | 22-01-2018	12.82 %
4Ab-HF | 25-09-2017	12.57 %
27B-HF | 18-06-2018	12.54 %
17A-HF | 12-03-2018	12.19 %
31B-HF | 01-10-2018	11.93 %
25A-HF | 05-03-2018	11.90 %
8B-HF | 11-12-2017	11.61 %
3A-HF | 28-08-2017	11.39 %
40A-HF | 07-01-2019	11.16 %
36A-HF | 27-08-2018	10.73 %
31A-HF | 13-08-2018	9.86 %
43B-HF | 05-12-2018	9.17 %
48B-HF | 21-12-2018	8.91 %
22B-HF | 28-05-2018	8.68 %
5A-HF | 18-09-2017	8.61 %
36B-HF | 15-10-2018	8.25 %
1B- | 02-10-2017	8.20 %
42B-HF | 19-11-2018	8.01 %
50A-HF | 26-11-2018	7.65 %
10B-HF | 18-12-2017	7.57 %
24B-HF | 04-06-2018	7.50 %
13B-HF | 13-04-2018	7.41 %
34B-HF | 14-11-2018	6.94 %
11B-HF | 15-01-2018	6.87 %
50B-HF | 14-01-2019	6.73 %
43A-HF | 15-10-2018	6.23 %
53A-HF | 02-01-2019	6.09 %
44A-HF | 29-10-2018	5.28 %
4A-HF | 25-09-2017	4.80 %
19A-HF | 19-02-2018	4.09 %
32B-HF | 29-06-2018	3.89 %
39A-HF | 15-10-2018	3.61 %
46A-HF | 19-11-2018	3.58 %
12A-HF | 06-12-2017	2.09 %
1A-HF | 14-08-2017	1.70 %
X-HC | 23-10-2017	1.32 %
8A-HF | 23-10-2017	1.30 %

Print schemes of removed points for all samples

## Warning: Removed 7 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

# Get Estimates

Bioenergetics and Estimates

Estimates

Estimates are taken by median of measurements from each interval in sample. Estimates are stored together with other details about the intervals in files called -Estimated_values.csv in OUTPUT folder.

If you want to see or change how they are computed see the function compute_bioenergetics_ ()in file analysis_source_functions.R

## `summarise()` has grouped output by 'sample_id'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'sample_id'. You can override using the `.groups` argument.

Example of estimates file

sample_id	Interval	mean	SD	SE	size	CV
10A-HF | 30-10-2017	Int1	47.648	3.929	0.349	127	8.246
10A-HF | 30-10-2017	Int2	17.073	2.187	0.265	68	12.812
10A-HF | 30-10-2017	Int3	109.375	6.082	1.075	32	5.560
10A-HF | 30-10-2017	Int4	15.170	1.553	0.194	64	10.234

See diferent distribution of the estimates

Bioenergetics

Bioenergetics are computed from estimates, there there are two sets of bioenergetics provided.

1. Ratio Based, computed from log(OCR) estimates Located in OUTPUT/LogOCR-BioEnergetics.csv

2. Difference based, computed from OCR esimates Located in OUTPUT/OCR-BioEnergetics.csv If you want to see or change how they are computed see the function compute_bioenergetics_() in file analysis_source_functions.R

Example of Bioenergetics file

Sample	variable	value
10A-HF | 30-10-2017	log.Basal.Resp	1.145
10B-HF | 18-12-2017	log.Basal.Resp	1.392
11-HC | 18-09-2017	log.Basal.Resp	1.275
11A-HF | 28-11-2017	log.Basal.Resp	1.012

Groups

If the sample_id of your samples contains letters A or B following graph will plot bioenergetics of the two grups. It is suggested to use this plot only when you are looking at grouped samples and project name shouldn’t contain letters A or B. Any sample that is not labeled will be part of B group.

You can change the A and B letters in the markdown code to compare other groups.

# difference based BE
n.bio %>% 
  mutate(Group = ifelse(grepl("A", Sample), "A", "B")) %>% # change "A" and "B" for any character or string
  ggplot(aes(Group, value, fill = variable))+
  ggtitle("OCR Normal scale Bio-Energetics biological groups ")+
  geom_boxplot(width = 0.5, outlier.size = -1, alpha = 0.7)+
  geom_jitter(width = 0.1, show.legend = FALSE, size = 0.5)+
  xlab("Bio-Energetics")+
  ylab("OCR")+
  facet_grid(. ~ variable ) +
  theme_bw()

# ratio based BE
l.bio %>% 
  mutate(Group = ifelse(grepl("A", Sample), "A", "B")) %>% # change "A" and "B" for any character or string
  ggplot(aes(Group, value, fill = variable))+
  ggtitle("OCR Normal scale Bio-Energetics biological groups ")+
  geom_boxplot(width = 0.5, outlier.size = -1, alpha = 0.7)+
  geom_jitter(width = 0.1, show.legend = FALSE, size = 0.5)+
  xlab("Bio-Energetics")+
  ylab("OCR")+
  facet_grid(. ~ variable ) +
  theme_bw()

———— ECAR ————

Filter out no relevant samples

## `summarise()` has grouped output by 'sample_id'. You can override using the `.groups` argument.

## [1] "Samples removed due lack of usable measurements(less than 8  in any interval)"

## [1] "19-HC | 25-09-2018"  "2-HC | 17-10-2017"   "34A-HF | 25-09-2018" "42A-HF | 01-10-2018" "50A-HF | 26-11-2018" "Y-HC | 28-11-2017"

## [1] "Samples removed because don't have 3 Intervals"

##  [1] "12A-HF | 06-12-2017"                          "15B-HF | 19-03-2018"                          "19-HC | 25-09-2018"                          
##  [4] "19A-HF | 19-02-2018"                          "31B-HF | 01-10-2018"                          "32Ab-HF | 15-05-2018"                        
##  [7] "3B-HF | 17-10-2017"                           "48B-HF | 21-12-2018"                          "4B-HF | 13-11-2017"                          
## [10] "7A-HF | 23-11-2017"                           "FCCP-CONBIS-29POST-29POST | 25-06-2018"       "Oligomycin-CONBIS-29POST-29POST | 25-06-2018"

Remove outliars

## 1  Point outliares:  804 -- 4.846293 % 
## 2  Point outliares:  331 -- 1.995178 % 
## 3  Point outliares:  152 -- 0.9162146 % 
## 4  Point outliares:  85 -- 0.5123568 % 
## 5  Point outliares:  40 -- 0.2411091 % 
## 6  Point outliares:  20 -- 0.1205546 % 
## 7  Point outliares:  8 -- 0.04822182 % 
## 8  Point outliares:  5 -- 0.03013864 % 
## 9  Point outliares:  6 -- 0.03616637 % 
## 10  Point outliares:  2 -- 0.01205546 % 
## 11  Point outliares:  0 -- 0 % 
## Total single point outliars:  8.758288 %

## `summarise()` has grouped output by 'sample_id'. You can override using the `.groups` argument.

Amount of removed outliers across samples

sample_id	percentage
1A-HF | 14-08-2017	36.63 %
49B-HF | 22-01-2019	31.78 %
32A-HF | 15-05-2018	29.37 %
40B-HF | 25-02-2019	26.02 %
32Ab- | 15-05-2018	25.89 %
2B_2-DB2 | 12-02-2018	19.09 %
15A-HF | 15-01-2018	19.02 %
55A-HF | 21-01-2019	18.83 %
22Bb-HF | 28-05-2018	18.65 %
33A-HF | 20-08-2018	17.67 %
11B_2-DB2 | 14-01-2019	17.45 %
10B-HF | 18-12-2017	17.33 %
46A-HF | 19-11-2018	17.14 %
3Ab-HF | 28-08-2017	17.06 %
26B-HF | 11-06-2018	15.48 %
53 post-ConBis | 20-02-2019	15.08 %
5B-HF | 06-11-2017	14.68 %
43B-HF | 05-12-2018	13.10 %
13B-HF | 13-04-2018	12.73 %
3A-HF | 28-08-2017	12.30 %
11A-HF | 28-11-2017	12.03 %
32B-HF | 29-06-2018	11.11 %
34B-HF | 14-11-2018	10.71 %
49A-HF | 05-12-2018	10.71 %
8A-HF | 23-10-2017	10.23 %
X-HC | 23-10-2017	10.05 %
1B- | 02-10-2017	9.55 %
50B-HF | 14-01-2019	9.30 %
53A-HF | 02-01-2019	9.24 %
31Bt-HF | 01-10-2018	9.17 %
Glycolysis-DB-26-26 | 19-02-2018	9.17 %
5A-HF | 18-09-2017	9.13 %
17B-HF | 30-04-2018	8.94 %
33B-HF | 08-10-2018	8.93 %
12B-HF | 22-01-2018	8.33 %
14B-HF | 12-03-2018	7.80 %
48 pre- | 05-11-2018	7.17 %
43A-HF | 15-10-2018	6.96 %
39 post-ConBis | 23-11-2018	6.81 %
22B-HF | 28-05-2018	6.75 %
29A-HF | 08-05-2018	6.50 %
14A-HF | 22-01-2018	6.45 %
8B-HF | 11-12-2017	6.38 %
44A-HF | 29-10-2018	5.95 %
36A-HF | 27-08-2018	5.74 %
Glycolysis-DB-26-26 | 06-12-2017	5.19 %
11B-HF | 15-01-2018	5.04 %
13A-HF | 26-02-2018	5.00 %
39A-HF | 15-10-2018	3.57 %
40A-HF | 07-01-2019	3.57 %
22A-HF | 09-04-2018	3.28 %
36B-HF | 15-10-2018	3.08 %
27Bb-HF | 18-06-2018	2.87 %
18A-HF | 12-02-2018	2.72 %
55B-HF | 25-02-2019	2.70 %
4 post-Conbis | 13-11-2017	2.50 %
25B-HF | 23-04-2018	2.38 %
18B-HF | 03-04-2018	2.13 %
19B-HF | 09-04-2018	1.98 %
42B-HF | 19-11-2018	1.36 %
9A_2-DB2 | 13-08-2018	1.32 %
4A-HF | 25-09-2017	1.23 %
10A-HF | 30-10-2017	1.19 %
17A-HF | 12-03-2018	0.93 %
48 post-ConBis | 21-12-2018	0.84 %
31A-HF | 13-08-2018	0.83 %
24B-HF | 04-06-2018	0.79 %
26A-HF | 23-04-2018	0.79 %
25A-HF | 05-03-2018	0.45 %
27A-HF | 30-04-2018	0.40 %

Estimates

Bioenergetics and Estimates

## `summarise()` has grouped output by 'sample_id'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'sample_id'. You can override using the `.groups` argument.

#Estimates

Bioenergetics

#Groups

ECAR vs OCR

BASAL ECAR vs BASAl OCAR